Martín Dias, Mariano Martinez Peck, Sté́phane Ducasse et al. · 2011
Serializing object graphs is an important activity since objects should be stored and reloaded on different environments. There is a plethora of frameworks to serialize objects based on recursive parsing of the object graphs. However such approaches are often too slow. Most approaches are limited in their provided features. For example, several serializers do not support class shape changes, global references, transient references or hooks to execute something before or after being stored or loaded. Moreover, to be faster, some serializers are not written taking into account the object-oriented paradigm and they are sometimes even implemented in the Virtual Machine hampering code portability. VM-based serializers such as ImageSegment are difficult to understand, maintain, and fix. For the final user, it means a serializer which is difficult to customize, adapt or extend to his own needs.
Cited by 13
Sam Maggs · 2014
Cited by 0
Wendy Roy · 2019
Cited by 0
Matthias Schlögl, Maximilian Kaiser, Ágoston Zénó Bernád et al. · Zenodo (CERN European Organization for Nuclear Research) · 2020
This is a trig serialisation of the enriched version of the Austrian Biographical Dictionary. The data was enriched during the Austrian Prosopographical Information System (APIS) project. It contains data on ~19.000 persons who had an impact on Austrian soil and died between 1815 and 1955. This version of the APIS data uses CIDOC CRM and is less complete than the also available JSON version of the data (using a custom data model). Please refer to Matthias Schlögl for any inquiries.
Cited by 0Open access
Bin Huang (331329), You Tang (5931773) · Figshare · 2021
Cited by 0Open access
Jiří Soukup, Petr Macháček · 2014
Cited by 2
Miller, Mark C. · Zenodo (CERN European Organization for Nuclear Research) · 2023
In the early days of scientific computing (think Fortran era), most application data took the form of large arrays. These are easily stored to any of many different file formats including HDF5. As scientific computing applications have evolved and grown in sophistication and design, large arrays still play a key role but so do highly complex, pointer-linked, multiply nested, data structures involving abstract data types. Any developer confronted with persisting these complex data structures to disk, especially in a way which is machine portable, ultimately seeks a body of code that is simple and easily adaptable as that data structure changes over the life of the application in which it is used. When that goal is pitted against the relatively deep documentation dive developers must take to fully understand what HDF5 is capable of and how best to apply it in any given circumstance, this often leads to the very naïve use of recursive traversal of data structures where tiny HDF5 datasets, groups and attributes are emitted. What makes perfect sense from an HDF5 API perspective turns into a horrendous disaster from an I/O performance perspective. This talk will describe the basic use case, the problems with the naïve approach as well as a much improved approach which though more complex to code is much more I/O performant.
Cited by 0Open access
Matthias Schlögl, Maximilian Kaiser, Ágoston Zénó Bernád et al. · Zenodo (CERN European Organization for Nuclear Research) · 2021
This is a trig serialisation of the enriched version of the Austrian Biographical Dictionary. The data was enriched during the Austrian Prosopographical Information System (APIS) project. It contains data on ~19.000 persons who had an impact on Austrian soil and died between 1815 and 1955. This version of the APIS data uses CIDOC CRM and is less complete than the also available JSON version of the data (using a custom data model). Please refer to Matthias Schlögl for any inquiries.
Cited by 0Open access
Brad E. Hollister · Apress eBooks · 2020
This chapter explains Blender’s persistent data model and related data structures. We first discuss a high-level description of the blend file. One of the most interesting aspects about the blend file is that it has been engineered to be very fast. Its contents are a binary image of memory, when the file was serialized. Blender supports backward compatibility, making the required transformations of the data structures in a given release to any later release. We will discuss the mechanics of reading a blend file. Because the blend file is a direct copy of runtime data structures, a good understanding of the blend file will support our understanding of how Blender maintains state and manages scene contents, such as objects and mesh data.
Cited by 0
Luc Moreau, Trung Dong Huynh · Lecture notes in computer science · 2021
Cited by 10
James, Dorothy J. · Americanae (AECID Library) · 1983
https://www.sil.org/resources/archives/22936
Cited by 1
Miller, Mark C. · Zenodo (CERN European Organization for Nuclear Research) · 2023
In the early days of scientific computing (think Fortran era), most application data took the form of large arrays. These are easily stored to any of many different file formats including HDF5. As scientific computing applications have evolved and grown in sophistication and design, large arrays still play a key role but so do highly complex, pointer-linked, multiply nested, data structures involving abstract data types. Any developer confronted with persisting these complex data structures to disk, especially in a way which is machine portable, ultimately seeks a body of code that is simple and easily adaptable as that data structure changes over the life of the application in which it is used. When that goal is pitted against the relatively deep documentation dive developers must take to fully understand what HDF5 is capable of and how best to apply it in any given circumstance, this often leads to the very naïve use of recursive traversal of data structures where tiny HDF5 datasets, groups and attributes are emitted. What makes perfect sense from an HDF5 API perspective turns into a horrendous disaster from an I/O performance perspective. This talk will describe the basic use case, the problems with the naïve approach as well as a much improved approach which though more complex to code is much more I/O performant.
Cited by 0Open access
Arthur Holmer · Lund University Publications (Lund University) · 2005
This paper presents the different kinds of serial verb constructions found in Kammu (a Mon-Khmer language spoken in northern Laos). Special focus is placed on how diagnostic tests (interrogative constructions, irrealis constructions and the scope of negation) can be applied to illustrate the contrast between the most prototypical serial verb constructions (e.g. directional coverbs and resultatives) and more peripheral constructions which share some of the properties of serial verb constructions (e.g. purpose constructions).
Cited by 2Open access
Sara Bouchenak, et al. · 2003
The Java system supports the transmission of code via dynamic class loading, and the transmission or storage of data via object serialization. However, Java does not provide any mechanism for the transmission/storage of computation (i.e., thread serialization). Several projects have recently addressed the issue of Java thread serialization, e.g., Sumatra, Wasp, JavaGo, Brakes, Merpati. But none of them has been able to avoid the overhead incurred by thread serialization on thread performance. We propose a Java thread serialization mechanism that does not impose any performance overhead on serialized threads. In this paper, we describe our implementation of thread serialization in Sun Microsystems' JVM, and present the techniques that allowed us to cancel the performance overhead, namely type inference and dynamic de-optimization.
Cited by 0
Hutchisson, Don · The COCOON platform (University of Paris) · 1987
This material was collected and analysed prior to the date of publication and has not been updated since then. It reflects the understanding of the researcher at that time.
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Javier D. Fernández, Miguel A. Martínez‐Prieto · Encyclopedia of Big Data Technologies · 2022
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Stephen R. G. Fraser · Apress eBooks · 2003
Most programs are of little use if there is no way of retrieving input from some source and outputting it to the same or another source. There are several options available for handling input/output (I/O). In this chapter, you will examine file and directory I/O, I/O manipulation and, finally, serialization or the saving of class for later retrieval.KeywordsFile SystemFile AccessPath ClassFile ClassPlayer ClassThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Cited by 1
Apress eBooks · 2009
Most programs are of little use if there is no way of retrieving input from some source and outputting it to the same or another source. You have several options available for handling input/output (I/O). In this chapter, you will examine file and directory I/O, I/O manipulation, and finally, serialization or the process of storing the state of an object or member to a permanent medium.
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Mario Tirino · 2026
This chapter examine the socio-economic context together with the technological, cultural, and social processes that shaped the modalities of seriality between the early 1900s and the 1930s. This period was characterized by three interrelated macro-phenomena:first, the definition of media formats, production and distribution models, and consumer cultures, which in various ways continued to reverberate throughout the Western world in the decades following World War II; second, the experimentation with new forms of transmedia serialization of narrative universes; and third, the construction of a technological imaginary that facilitated the transfer of the 19th-century literary and iconographic heritage into the domain of the electrified experience of the world, enabled above all by cinema and radio.
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Hossam Fattah · 2021
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